Using Google Maps road traffic estimations to unfold spatial and temporal inequalities of urban road congestion: A pilot study from Budapest

Abstract

In recent urban geography literature, smart cites became a fashionable subject. The smart city paradigm is strongly connected to researches based on big data. The main objective of this paper is to strengthen the idea of usefulness of big data in examining and developing the urban transportation system as part of smart cities. In this pilot research, Google Maps traffic estimation data were used to evaluate the special vehicular traffic flows in District 3, in Budapest. On 45 defined road sections, travel time estimation data were collected with the aim to calculate the extent of wasted time in traffic jams. According to our results this source of 'big data' is a feasible way of conducting 'smart' research on a city road system. The most relevant advantage of this database is that it is continually generated on a high spatial and temporal resolution. The conclusion of this pilot research is that spatial and temporal inequalities are evincible from this database, unrecognized processes can be easily analysed, which can help urban planners to rethink their strategies on urban transport system. The most important findings showed that, on workdays, there is a second wave of peak traffic on many roads, and, within our chosen district, there are congestion hot spot places. It is important to note that Google Maps data have limits, but by understanding them this method is a useful way for geographers to examine urban traffic congestion patterns with a high spatial resolution.

Combining Social Media and Mobile Positioning Data in the Analysis of Tourist Flows: A Case Study from Szeged, Hungary

“…This knowledge can help local stakeholders organize specific tourist events outside the peak season and better regulate tourist flows both spatially and temporally to mitigate possible conflicts related to tourism (e.g., crowding in public spaces, noise, and overtourism at certain locations) [61][62][63][64]. Festivals and events may lead to crowding in specific areas [65]; therefore, such knowledge is important not only for the planning of tourism development but also for sustainable infrastructure and transport planning and environmental management in city regions.…”

Network Congestion Diffusion Model Considering Congestion Distribution Information

“…However, some researchers believe that perhaps network congestion diffusion would be worse when users with the congestion distribution information selfishly and unwisely choose another longer way, which will cause more capacity occupancy and intensify congestion diffusion. This means that the congestion distribution information will not alleviate network congestion diffusion absolutely due to various influencing factors and complex relationships [25]. No conclusion can be made that congestion distribution information from Google or Baidu maps has a direct positive influence on network congestion diffusion because there are no reliable evidences [26].…”